
\section{Progress}
	Till now, we have implemented MFCC feature extractor and GMM model for
	acoustic modeling. For GMM we employ scikit-learn\cite{scikit-learn}.

	We've tested on 20 speakers for a closed set recognition task and calculate
	accuracy of recognition. 30 seconds utterance is used for training, and 5
	seconds utterance is used for test. We randomly extract 100 continuous 5-second
	test utterance from utterance of a speaker as test set.
	There's no overlap between training and test utterance.

	For the last week, we fine-tuned parameters of our model and scrutinizing more
	paper preceding to our work.

	The test is repeated 20 times to obtain an accurate estimate of accuracy.
	The test result is as follows:

	\begin{itemize}
		\item \textbf{Spontaneous} 0.940
		\item \textbf{Reading} 0.926
		\item \textbf{Whisper} 0.931
	\end{itemize}
